ArticleSensors (Basel, Switzerland)2022
Stroke Risk Prediction with Machine Learning Techniques.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 3 of them syntheses that pooled it.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
51 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Evaluation of machine learning models for stroke risk prediction in atrial fibrillation: a systematic review and meta-analysis.BMC cardiovascular disorders · 2026Pooled it
- [Organization and costs of stroke care in outpatient settings: Systematic review].Atencion primaria · 2023Pooled it
- Predictive Performance of Artificial Intelligence Models for Stroke Risk Stratification: A Systematic Review and Meta-Analysis.Inquiry : a journal of medical care organization, provision and financingPooled it
- Artificial Intelligence and Emerging Digital Technologies Across the Stroke Continuum: From Risk Prediction to Real-Time Monitoring and Rapid Response.Medicina (Kaunas, Lithuania) · 2026Review
- An explainable ensemble learning-based auxiliary diagnosis system for cerebral small vessel disease.Scientific reports · 2026Article
- Explainable multi-modal approach for uncovering key predictors of stroke-risk from ECG, EMG, blood pressure, and respiratory signals.Scientific reports · 2026Article
- HMLA: A hybrid machine learning approach for enhancing stroke prediction models with missing data imputation techniques.Scientific reports · 2025Article
- Article
- Anston attentional network for structured data based stroke risk prediction in smart aging.Scientific reports · 2025Article
- Machine learning for stroke prediction using imbalanced data.Scientific reports · 2025Article
- Development and validation of a predictive model to forecast the cerebrovascular risk burden in older people using the NACC and ROSMAP dataset.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Machine learning-based prediction of preterm birth risk using methylation changes in neonatal cord blood CpG sites.BMC pregnancy and childbirth · 2025Article
- Stroke prediction in elderly patients with atrial fibrillation using machine learning combined clinical and left atrial appendage imaging phenotypic features.BMC cardiovascular disorders · 2025Article
- Optimizing Stroke Risk Prediction: A Primary Dataset-Driven Ensemble Classifier With Explainable Artificial Intelligence.Health science reports · 2025Article
- Optimized machine learning mechanism for big data healthcare system to predict disease risk factor.Scientific reports · 2025Article
- A Novel Explainable Attention-Based Meta-Learning Framework for Imbalanced Brain Stroke Prediction.Sensors (Basel, Switzerland) · 2025Article
- Risk-based evaluation of machine learning-based classification methods used for medical devices.BMC medical informatics and decision making · 2025Article
- Urban and rural disparities in stroke prediction using machine learning among Chinese older adults.Scientific reports · 2025Article
- Improving stroke risk prediction by integrating XGBoost, optimized principal component analysis, and explainable artificial intelligence.BMC medical informatics and decision making · 2025Article
- A comprehensive analysis of stroke risk factors and development of a predictive model using machine learning approaches.Molecular genetics and genomics : MGG · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A stroke is caused when blood flow to a part of the brain is stopped abruptly. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. The main contribution of this study is a stacking method that achieves a high performance that is validated by various metrics, such as AUC, precision, recall, F-measure and accuracy. The experiment results showed that the stacking classification outperforms the other methods, with an AUC of 98.9%, F-measure, precision and recall of 97.4% and an accuracy of 98%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.